A minimum cover approach for extracting the road network from airborne LIDAR data

We address the problem of extracting the road network from large-scale range datasets. Our approach is fully automatic and does not require any inputs other than depth and intensity measurements from the range sensor. Road extraction is important because it provides contextual information for scene analysis and enables automatic content generation for geographic information systems (GIS). In addition to these two applications, road extraction is an intriguing detection problem because robust detection requires integration of local and long-range constraints. Our approach segments the data based on both edge and region properties and then extracts roads using hypothesis testing. Road extraction is formulated as a minimum cover problem, whose approximate solutions can be computed efficiently. Besides detecting and extracting the road network, we also present a technique for segmenting the entire city into blocks. We show experimental results on large-scale data that cover a large part of a city, with diverse landscapes and road types.